Artificial Intelligence for healthcare Professionals - Part 1: Foundations and Core Concepts

• Professional background in healthcare
• No technical or programming background required

Artificial Intelligence is increasingly shaping modern healthcare systems, supporting clinicians in areas such as diagnostics, treatment planning, patient monitoring, and healthcare delivery optimization. From AI-assisted radiology to predictive analytics and digital health platforms, these technologies are becoming part of everyday clinical practice.

However, despite their growing presence, many healthcare professionals lack a clear understanding of how AI systems function, what their limitations are, and how they should be critically evaluated before integration into clinical workflows.

This training provides a structured introduction to Artificial Intelligence in healthcare. It demystifies core concepts such as machine learning, neural networks, and data-driven systems, and explains how these technologies are applied across clinical contexts.

Participants explore how AI supports healthcare professionals rather than replacing them, with emphasis on clinical responsibility, patient safety, and the importance of human oversight. The course highlights real-world healthcare applications including diagnostic systems, clinical decision-support tools, and digital health infrastructures.

By the end of the training, participants will have a clear and grounded understanding of Artificial Intelligence in healthcare and will be equipped to critically assess AI tools and their role in clinical environments.

Day 1: Foundations of Artificial Intelligence in Healthcare

Artificial Intelligence Demystified

• Definitions, myths, and realities of Artificial Intelligence
• Historical evolution of AI and digital technologies in medicine
• AI paradigms: symbolic AI, machine learning, and data-driven systems
• Narrow AI vs general AI and current capabilities in healthcare

How AI Systems Learn

• Human intelligence vs artificial intelligence
• Learning paradigms: supervised, unsupervised, and reinforcement learning
• Conceptual understanding of artificial neural networks
• The role of data in training AI systems

Data in Healthcare AI

• Medical datasets and electronic health records
• Data quality, bias, and limitations of healthcare AI models
• Interpreting AI outputs in clinical environments

Day 2: AI Applications in Healthcare Practice

AI-Assisted Diagnostics

• medical imaging and radiology
• pathology and laboratory analysis
• early disease detection and predictive models

Clinical Decision Support Systems

• AI tools supporting diagnosis and treatment planning
• predictive risk modeling and patient stratification
• AI in hospital workflow optimization

Digital Health and Patient Monitoring

• wearable health devices and digital biomarkers
• remote patient monitoring technologies
• AI-supported analysis of physiological signals

By the end of the course, participants will be able to:

• understand fundamental AI concepts and learning mechanisms
• Identify key AI applications in healthcare
• Interpret AI outputs in clinical contexts
• Critically evaluate AI tools used in healthcare
• Understand limitations and risks of AI systems
• Navigate ethical, regulatory, and data protection requirements related to healthcare AI

• lectures introducing key AI concepts
• real-world healthcare examples
• guided discussions and interaction
• practical interpretation of clinical AI systems

Certificate of attendance

This training provides a clear and practical introduction to Artificial Intelligence (AI) for healthcare professionals. Participants learn how AI systems work, where they are currently used in healthcare, and how clinicians can critically evaluate and responsibly integrate these technologies into clinical practice.